Here we conduct experiments to give a quantitative analysis on the misalignments in conventional cycle training of unsupervised visual tracking.
In our manuscript, we claim that the misalignment in cycle training severely hinders the performance of unsupervised visual tracking.
Detailly, the source of this misalignment is the mismatch between the internal template and search region feature. For delving deep into this insight, we conduct a quantitative analysis of the impact of this misalignment.
We choose the classical Siamese tracker, SiamRPN++ [1]}, as the baseline. For simplification, the mismatch in intermediate frames, which may be produced by forward tracking errors or initialization bias, is simulated as noises added to ground-truth for cropping the template patches in SiamRPN++ [1]}. Specifically, we add noises to template patches with the following operation.
Let us denote the ground-truth boxes in template frames as \((cx, cy, w, h)\) , where \((cx, cy)\)  in the center coordinates of bounding boxes, \(w\)  and \(h\)  are the width and height of the boxes respectively.
The jittered template bounding boxes can be denoted as \((cx + \sigma _1 w, cy + \sigma _2 h, (1 + \sigma _3) w, (1 + \sigma _4) h)\) , where \(\sigma _{k}\)  denotes random number between \(-0.5\)  and \(0.5\)  generated from uniform distribution.
